Tools

"... We advocate a new learning task that deals with orders of items, and we call this the Learning from Order Examples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples that are o ..."

We advocate a new learning task that deals with orders of items, and we call this the LearningfromOrderExamples (LOE) task. The aim of the task is to acquire the rule that is used for estimating the proper order of a given unordered item set. The rule is acquired from training examples

"... This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibili ..."

, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper

"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning

Abstract—We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face ” and “nonface ” model clusters. At each image location, a difference feature

"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic context-free grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a first-order r ..."

"... …Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer ..."

…Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer

"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."

tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our

"... In which we try to give a basic intuitive sense of what reinforcement learning is and how it differs and relates to other fields, e.g., supervised learning and neural networks, genetic algorithms and artificial life, control theory. Intuitively, RL is trial and error (variation and selection, search ..."

"... Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a self-evident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows that e ..."

Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a self-evident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows that economics is – or should be – about individual happiness. In particular, the question is how do economic growth, unemployment and inflation, as well as institutional factors such as good governance, affect individual well-being? In addition to this intrinsic interest, there are three major reasons for economists to consider happiness. The first is economic policy. At the micro-level, it is often impossible to make a Pareto-optimal proposal, because a social action entails costs for some individuals. Hence an evaluation of the net effects, in terms of individual utilities, is needed. On an aggregate level, economic policy must deal with trade-offs, especially those between unemployment and

"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with &quot;flat &quot; data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."

of the relational structure present in our database. This paper builds on the recent work on probabilistic relational models (PRMs), and describes how to learn them from databases. PRMs allow the properties of an object to depend probabilistically both on other properties of that object and on properties of related